Deep learning method, device and system for predicting service life of lithium battery

A life prediction, lithium battery technology, applied in the field of lithium batteries, can solve the problem of discontinuous prediction results

Pending Publication Date: 2020-12-01
BEIHANG UNIV
View PDF2 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a deep learning method, device and system for lithium battery life prediction, so as to improve the accuracy of battery life pred

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep learning method, device and system for predicting service life of lithium battery
  • Deep learning method, device and system for predicting service life of lithium battery
  • Deep learning method, device and system for predicting service life of lithium battery

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0062] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0063] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects and not necessarily Describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a deep learning method, device and system for predicting the service life of a lithium battery. The method comprises steps of obtaining data of a to-be-detected battery; inputting the to-be-detected battery data into an auto-encoder, and outputting dimension raising characteristic data; taking a matrix feature map corresponding to the dimension raising feature data as inputof a target battery network model; wherein the target battery network model is a network for predicting the service life of the to-be-detected battery data according to a one-dimensional feature vector and a time domain feature vector of the matrix feature map; and outputting a predicted value corresponding to the to-be-detected battery data through the target battery network model. The method isadvantaged in that accuracy of battery life prediction can be improved, a problem of discontinuity in a prediction result can be solved, noise points of a prediction curve are reduced, and a smooth and stable continuous prediction curve is obtained.

Description

technical field [0001] The invention relates to the technical field of lithium batteries, in particular to a deep learning method, device and system for life prediction of lithium batteries. Background technique [0002] Lithium battery is a kind of green high-energy rechargeable battery. It is widely used in electronic communication engineering, transportation and aerospace fields because of its high capacity, low self-discharge rate, high safety and long cycle life. However, the failure of lithium batteries may lead to a decline in the performance of electrical equipment, and with the increase in the service life of charging and discharging, the use characteristics of lithium batteries will gradually decline, shorten the service life of lithium batteries, and even cause serious failure accidents. Therefore, the prediction of lithium battery remaining useful life (Remaining Useful Life, RUL) is particularly important. [0003] The existing deep learning methods for life pr...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F30/27G01R31/392G06N3/04G06N3/08G06F119/04
CPCG01R31/392G06N3/082G06N3/045Y02E60/10
Inventor 任磊赵力
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap